Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning. In particular, we investigate multiple ways of enforcing the epipolar constraint in flow estimation. To alleviate a "chicken-and-egg" type of problem encountered in dynamic scenes where multiple motions may be present, we propose a low-rank constraint as well as a union-of-subspaces constraint for training. Experimental results on various benchmarking datasets show that our method achieves competitive performance compared with supervised methods and outperforms state-of-the-art unsupervised deep-learning methods.Comment: CVPR 201

On optimization of the observation station based on the effective independence method

Model reduction of large-scale structures has been improved and a weight coefficient reflecting the contribution proportion of a higher-order model has been introduced contribution based on the shortcomings of conventional optimization algorithms, with the aim to solve the problem that conventional optimization algorithms do not serve the optimized distribution of large-scale structure observation stations. Therefore, two hybrid optimization algorithms are proposed based on the effective independence method. The effective independence-average modal kinetic/strained energy coefficient methods have been compared with effective independence method for Guyan reduction based on modal kinetic/strained coefficients and the respectively improved ones through a GARTEUR plane simulation experiment. Results have shown that both of the two algorithms effectively avoided the emergence of concentrated observation stations, best ensured the contribution of all modal kinetic and strain energy and the requirements that the better-arranged observation station has much more strained energy. Model tests were also made on the two methods by employing real GARTEUR plane, which showed that the two algorithms guaranteed the completeness and linear independence of monitoring mode and that the two are of great practical value to the observation station optimization and distribution of large-scale structures